CASSFormer: A Lightweight CNN-Transformer Hybrid Network for Real-Time Coronary Artery Stenosis Detection and Localization

Coronary angiography (CAG) is the clinical gold standard for assessing coronary artery stenosis. However, its two-dimensional projection nature often causes vessel overlap and limited contrast, making subtle or complex stenotic regions difficult to detect and localize, thereby increasing reliance on experienced cardiologists. Existing deep learning methods face a practical trade-off: CNN-based models struggle with low-contrast or small-scale lesions, while Transformer-based models incur high computational cost, limiting real-time applicability. To address these challenges, we propose Coronary Angiography Structure-aware Stenosis Transformer (CASSFormer), a lightweight CNN-Transformer hybrid network for real-time coronary artery stenosis detection and localization. Specifically, CASSFormer integrates three key components: (i) a multi-stage spatial feature downsampling module (MSFD) to preserve fine-grained vascular textures during early feature extraction; (ii) a multi-scale mixed-topology block (MMTB) to model elongated vessel structures and multi-scale stenotic patterns; (iii) a dual-attention cross-scale adaptive fusion module (DCAF) that jointly exploits local directional attention and global channel attention to enhance sensitivity to low-contrast and small-scale stenotic regions. Experiments on a large private clinical dataset and two public datasets (ARCADE, ADSD) show that CASSFormer achieves favorable detection and localization performance compared with mainstream detection models, while maintaining low computational overhead (27.1 GFLOPs, 11.9M parameters) and an average inference time of 8.0 ms per image. Visualization results further suggest its robustness under challenging angiographic conditions, including vessel overlap, distal small vessels, and severe stenosis or vessel interruption associated with myocardial infarction. These results support CASSFormer as an efficient assistive tool for stenosis detection and localization in complex CAG scenarios.